Neural network introduction, feed-forward network backpropagtion optimization.
Where v i is the real-valued data of visible neuron i, h j is the binary data of hidden neuron j, b i v and b j h are biases of visible and hidden neurons, respectively, w ij are the weights connecting visible and hidden.
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Joint probability energy function form. Edge weight., CNN fully connected layer, sparse weight weight. P(h_j cv) frac1Z exp(-sum_i a_i v_i - sum_ellneq j b_ell h_ell - b_j * c - sum_i sum_ellneq j v_i w_iell h_ell - sum_i v_i w_ij * c) p(h_j 1v) fracp(h_j 1v)p(h_j 1v) p(h_j 0v) frac11 exp(-b_j-sum_i v_i w_ij) sigma(b_jsum_i v_i w_ij)., conditional probability sigmoid. W_ij fracpartial E(v,h)partial w_ij v_i h_j travelex cashback v_i h_j p(hv) p(v,h) expectation gradient. Layer 1 neural network deep learning.
Complexity filter CNN : sparse interactions ( sparse weight parameter sharing ( tied wieght equivariant representations., CNN layer layer connection (sparse weight weight random variable update weight group weight parameter share (parameter sharing). RBM learning restricted., layer connection p(hv) prod_j p(h_jv) leanring. Deep learning framework input filter filter image convolution preprocessing feature map machine learning framework input. ImageNet dataset, top-5 error.
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